--- name: rag-hybrid-search-d601a2eef391 description: Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies. allowed-tools: Read, Grep, Write, Edit, Bash, Glob, WebFetch graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:retrieval-augmented-generation, skill-area:search-indexing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # rag-hybrid-search Implement hybrid search combining semantic vector retrieval with keyword-based BM25 search for improved RAG pipeline accuracy and recall. ## Overview Hybrid search addresses the limitations of pure semantic or pure keyword search: - Semantic search excels at conceptual similarity but may miss exact matches - Keyword search finds exact terms but lacks semantic understanding - Hybrid combines both for superior retrieval performance ## Capabilities ### Search Strategies - Dense vector semantic search (embeddings) - Sparse vector keyword search (BM25, TF-IDF) - Hybrid fusion with configurable weighting - Reciprocal Rank Fusion (RRF) combination ### Retrieval Configuration - Configure embedding models for dense search - Tune BM25 parameters (k1, b values) - Set retrieval limits and thresholds - Apply metadata filtering ### Ranking & Reranking - Score normalization across search types - Weighted score fusion - Cross-encoder reranking - MMR (Maximum Marginal Relevance) diversity ### Index Management - Create and update hybrid indexes - Batch indexing with progress tracking - Index optimization and maintenance - Multi-index federation ## Usage ### Basic Hybrid Search with LangChain ```python from langchain_community.retrievers import BM25Retriever from langchain_community.vectorstores import Chroma from langchain.retrievers import EnsembleRetriever from langchain_openai import OpenAIEmbeddings # Create documents docs = [...] # Your document chunks # Dense retriever (semantic) embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(docs, embeddings) dense_retriever = vectorstore.as_retriever(search_kwargs={"k": 5}) # Sparse retriever (BM25) bm25_retriever = BM25Retriever.from_documents(docs) bm25_retriever.k = 5 # Hybrid ensemble hybrid_retriever = EnsembleRetriever( retrievers=[bm25_retriever, dense_retriever], weights=[0.4, 0.6] # Adjust based on use case ) # Query results = hybrid_retriever.invoke("How do I configure the system?") ``` ### Reciprocal Rank Fusion ```python def reciprocal_rank_fusion(results_lists: list, k: int = 60) -> list: """ Combine multiple ranked lists using RRF. k is a constant (typically 60) for smoothing. """ fused_scores = {} for results in results_lists: for rank, doc in enumerate(results): doc_id = doc.metadata.get("id", str(doc.page_content[:50])) if doc_id not in fused_scores: fused_scores[doc_id] = {"doc": doc, "score": 0} fused_scores[doc_id]["score"] += 1 / (k + rank + 1) # Sort by fused score sorted_docs = sorted( fused_scores.values(), key=lambda x: x["score"], reverse=True ) return [item["doc"] for item in sorted_docs] # Use with multiple retrievers semantic_results = dense_retriever.invoke(query) keyword_results = bm25_retriever.invoke(query) hybrid_results = reciprocal_rank_fusion([semantic_results, keyword_results]) ``` ### Pinecone Hybrid Search ```python from pinecone import Pinecone from pinecone_text.sparse import BM25Encoder # Initialize Pinecone pc = Pinecone(api_key="your-api-key") index = pc.Index("hybrid-index") # Prepare sparse encoder bm25 = BM25Encoder() bm25.fit(corpus) # Fit on your document corpus def hybrid_query(query: str, alpha: float = 0.5, top_k: int = 10): """ Query with hybrid search. alpha: weight for dense vectors (1-alpha for sparse) """ # Get dense embedding dense_embedding = embeddings.embed_query(query) # Get sparse embedding sparse_embedding = bm25.encode_queries([query])[0] # Hybrid query results = index.query( vector=dense_embedding, sparse_vector=sparse_embedding, top_k=top_k, include_metadata=True ) return results ``` ### Weaviate Hybrid Search ```python import weaviate client = weaviate.Client("http://localhost:8080") def weaviate_hybrid_search(query: str, alpha: float = 0.5, limit: int = 10): """ Weaviate native hybrid search. alpha: 0 = pure BM25, 1 = pure vector """ result = ( client.query .get("Document", ["content", "title", "metadata"]) .with_hybrid( query=query, alpha=alpha, properties=["content", "title"] ) .with_limit(limit) .do() ) return result["data"]["Get"]["Document"] ``` ## Task Definition ```javascript const ragHybridSearchTask = defineTask({ name: 'rag-hybrid-search-setup', description: 'Configure hybrid search for RAG pipeline', inputs: { vectorStore: { type: 'string', required: true }, // 'pinecone', 'weaviate', 'chroma', etc. embeddingModel: { type: 'string', default: 'text-embedding-3-small' }, bm25Params: { type: 'object', default: { k1: 1.5, b: 0.75 } }, fusionStrategy: { type: 'string', default: 'rrf' }, // 'rrf', 'weighted', 'custom' denseWeight: { type: 'number', default: 0.6 }, topK: { type: 'number', default: 10 } }, outputs: { retrieverConfigured: { type: 'boolean' }, indexStats: { type: 'object' }, artifacts: { type: 'array' } }, async run(inputs, taskCtx) { return { kind: 'skill', title: `Configure hybrid search with ${inputs.vectorStore}`, skill: { name: 'rag-hybrid-search', context: { vectorStore: inputs.vectorStore, embeddingModel: inputs.embeddingModel, bm25Params: inputs.bm25Params, fusionStrategy: inputs.fusionStrategy, denseWeight: inputs.denseWeight, topK: inputs.topK, instructions: [ 'Validate vector store connection and configuration', 'Set up dense embedding pipeline', 'Configure BM25/sparse encoding', 'Implement fusion strategy', 'Test retrieval quality with sample queries', 'Document configuration and tuning parameters' ] } }, io: { inputJsonPath: `tasks/${taskCtx.effectId}/input.json`, outputJsonPath: `tasks/${taskCtx.effectId}/result.json` } }; } }); ``` ## Applicable Processes - rag-pipeline-implementation - advanced-rag-patterns - knowledge-base-qa - vector-database-setup ## External Dependencies - Vector database (Pinecone, Weaviate, Chroma, Milvus, Qdrant) - Embedding provider (OpenAI, Cohere, Hugging Face) - BM25 encoder (rank_bm25, pinecone-text) ## References - [Claude Context (Zilliz)](https://github.com/zilliztech/claude-context) - Hybrid search MCP - [MCP Local RAG](https://github.com/shinpr/mcp-local-rag) - Local-first RAG with hybrid search - [LangChain Anthropic MCP Server](https://glama.ai/mcp/servers/@spencer-life/langchain-anthropic-mcp-server) - [Pinecone Hybrid Search](https://docs.pinecone.io/docs/hybrid-search) - [Weaviate Hybrid Search](https://weaviate.io/developers/weaviate/search/hybrid) ## Related Skills - SK-RAG-001 rag-chunking-strategy - SK-RAG-004 rag-reranking - SK-RAG-005 rag-query-transformation - SK-VDB-001 through SK-VDB-005 (vector database integrations) ## Related Agents - AG-RAG-001 rag-pipeline-architect - AG-RAG-003 vector-db-specialist - AG-RAG-004 retrieval-optimizer --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering, Marketing, Product Management Roles: Data Analyst, Data Scientist, Growth Marketer, ML / AI Engineer, Product Manager Specialties: API, Backend Tools: Langchain, Pinecone Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 4bb71588a346d97a0afc890c3f8b9b30fc2e1da43585eab63a293dda27452dec